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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

The type of cognitive function and the factors that influence it in the older adults with mild cognitive impairment:a

Xinru Qi¹, Fengyi Sun¹, Haiyan Yin¹

¹School of Nursing, Nanjing University of Chinese Medicine. No. 138 Xianlin Avenue, Qixia District, Nanjing, Jiangsu Province 210023, China.

Geriatric Nursing (New York, N.Y.)

|July 15, 2025

Abstract

Objectives:

In order to design targeted interventions, it is necessary to analyze the cognitive function profiles and the factors affecting individuals with mild cognitive impairment.

Methods:

From May to August 2022, 389 cases of community-based MCI in older adults were chosen using a stratified convenience sampling method in the Jiangsu Province cities of Nanjing, Changzhou, Nantong, and Xuzhou. The survey was conducted using the following instruments: a general information questionnaire, the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), the Pittsburgh Sleep Quality Index (PSQI), Patient Health Questionnaire-9(PHQ-9), The FRAIL Scale and Activity of Daily Living Scale (ADL).

Result:

Based on the latent profile analysis results, four potential categories for the type of MCI cognitive impairment were identified: high cognition-overall good group (Class 1, n=292, 75.1 %), low cognition-dementia high risk group (Class 2, n=22, 5.7 %), moderate cognition-disorientation group (Class 3, n= 43, 11.1 %), moderate cognition-naming disorder group (Class 4, n=32, 8.2 %). Among people with various subtypes of cognitive function, there were statistically significant distinctions in sex, smartphone use, and frequency of tea consumption (P< 0.05). Considering the high cognition-overall good group as a reference, the multinomial logistic regression analysis's findings showed that smartphone use[ low cognition-dementia high risk group: no, OR=3.177, 95 %CI(1.275, 7.919); moderate cognition-naming disorder group: no, OR=3.035, 95 %CI(1.404, 6.562)] and sex[ moderate cognition-naming disorder group: male, OR=0.311, 95 %CI(0.120, 0.802)] were significant factors in the various cognitive function subtypes of older adults with MCI (P < 0.05).

Conclusion:

The MCI cognitive function subtypes in older adults show significant group variability, and personalized interventions can be used to stop or postpone the onset of dementia in individuals with different subtypes.

Keywords:

Cognitive function Influence factor Latent profile analysis Mild cognitive impairment Naming ability Orientation

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